Stratification Tool for Opioid Risk Mitigation (STORM)

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Health and Medical

Operational functions

Forecasting and prediction, Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

Patients exposed to opioid drugs often have complex medical needs and are at risk of multiple negative outcomes, including overdose, suicide, development of addiction, and other behavioral health challenges. Numerous strategies have been developed to minimize risks and treat conditions that underly them, but coordination of care across patient treatment providers and conditions can be challenging and interventions to reduce risk can be time consuming. Decision support to systems are needed to ensure recognition of a patient’s current conditions and consideration and tracking of recommended interventions. Risk estimation is needed to ensure that the most complex and at-risk patients receive adequate clinical attention in time-constrained health care environments.

Expected benefits

The effectiveness of the targeted prevention program was tested during a randomized staged roll-out of the targeted prevention program using a stepped wedge evaluation design. The targeted prevention program utilizes the STORM model to identify patients for interdisciplinary case review (i.e. by a team of clinicians which includes those with pain, behavioral health and recovery expertise). Inclusion in the mandate for team-based case review of patients, the predictive model estimated that "very high" risk of overdose or suicide events in the next year was associated with a significant 22% reduction in all-cause mortality within 4 months of inclusion (Strombotne et al., 2023). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060407/ Inclusion in the case review program was also found to reduce all-cause mortality and reduce likelihood of opioid analgesic discontinuation, in the subpopulation of patients on long-term opioid analgesics (Li et al., 2023).

System outputs

The STORM predictive model provides an estimate of the likelihood of an overdose, suicide event, or death in the next year, documented in a health care system.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Not reported

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported